![International Journal of Advances in Applied Sciences (IJAAS)
Vol. 9, No. 4, December 2020, pp. 265~269
ISSN: 2252-8814, DOI: 10.11591/ijaas.v9.i4.pp265-269 265
Journal homepage: http://ijaas.iaescore.com
Chaotic based Pteropus algorithm for solving optimal reactive
power problem
Lenin Kanagasabai
Department of EEE, Prasad V. Potluri Siddhartha Institute of Technology, India
Article Info ABSTRACT
Article history:
Received Jan 8, 2020
Revised May 31, 2020
Accepted Jun 9, 2020
In this work, a Chaotic based Pteropus algorithm (CPA) has been proposed
for solving optimal reactive power problem. Pteropus algorithm imitates
deeds of the Pteropus. Normally Pteropus while flying it avoid obstacles by
using sonar echoes, particularly utilize time delay. To the original Pteropus
algorithm chaotic disturbance has been applied and the optimal capability of
the algorithm has been improved in search of global solution. In order to
augment the population diversity and prevent early convergence, adaptively
chaotic disturbance is added at the time of stagnation. Furthermore,
exploration and exploitation capability of the proposed algorithm has been
improved. Proposed CPA technique has been tested in standard IEEE 14,300
bus systems & real power loss has been considerably reduced.
Keywords:
Chaotic Pteropus behaviour
Optimal reactive power
Transmission loss
This is an open access article under the CC BY-SA license.
Corresponding Author:
Lenin Kanagasabai,
Department of EEE,
Prasad V. Potluri Siddhartha Institute of Technology,
Kanuru, Vijayawada, Andhra Pradesh, 520007, India.
Email: gklenin@gmail.com
1. INTRODUCTION
To have secure & economic, operations of the power system optimal reactive power problem plays
a prime role. Numerous conventional methods [1-6] have been successfully solved the problem. But
difficulty found in handling the inequality constraints. Various types of evolutionary algorithms [7-18]
applied to solve the problem. This paper projects chaotic Pteropus algorithm (CPA) for solving reactive
power problem. Pteropus algorithm is designed based on the actions of Pteropus. while flying it avoid
obstacles by using sonar echoes, particularly utilize time delay; happened while release and reflection of echo
which has been utilized during the period of for course-plotting. In Projected algorithm echolocation feature
is utilized in the algorithm and chaos theory intermingled in the flowing process. In order to augment
the population diversity and prevent early convergence, adaptively chaotic disturbance 𝑃𝑐 is added at the time
of stagnation. Projected CPA algorithm has been tested in standard IEEE 14,300 bus systems & simulation
study show the best performance of the projected algorithm in reducing the real power loss.
2. PROBLEM FORMULATION
The key objective of the reactive power problem is to minimize the system real power loss &
given as,
Ploss= ∑ gk(Vi
2
+Vj
2
−2Vi Vj cos θij
)
n
k=1
k=(i,j)
(1)](https://image.slidesharecdn.com/03-20276-37709-1-ed-chaoticbased-editmukti-201021072836/85/Chaotic-based-Pteropus-algorithm-for-solving-optimal-reactive-power-problem-1-320.jpg)
![ ISSN: 2252-8814
Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 265 – 269
266
Voltage deviation magnitudes (VD) is stated as follows,
Minimize VD = ∑ |Vk − 1.0|nl
k=1 (2)
Load flow equality constraints:
PGi – PDi − Vi ∑ Vj
nb
j=1
[
Gij cos θij
+Bij sin θij
] = 0, i = 1,2 … . , nb (3)
QGi − QDi − Vi ∑ Vj
nb
j=1
[
Gij sin θij
+Bij cos θij
] = 0, i = 1,2 … . , nb (4)
Inequality constraints are:
VGi
min
≤ VGi ≤ VGi
max
, i ∈ ng (5)
VLi
min
≤ VLi ≤ VLi
max
, i ∈ nl (6)
QCi
min
≤ QCi ≤ QCi
max
, i ∈ nc (7)
QGi
min
≤ QGi ≤ QGi
max
, i ∈ ng (8)
Ti
min
≤ Ti ≤ Ti
max
, i ∈ nt (9)
SLi
min
≤ SLi
max
, i ∈ nl (10)
3. PTEROPUS ALGORITHM
Pteropus algorithm imitates deeds of the Pteropus. Normally Pteropus while flying it avoid obstacles
by using sonar echoes, particularly utilize time delay; happened while release and reflection of echo which
has been utilized during the period of for course-plotting. Generalized rules for Pteropus algorithm are:
a. To sense the distance- all Pteropus use echolocation
b. In arbitrarily mode Pteropus fly with velocity ϑi at position yi with a fixed frequencyfmin, varying
wavelength λ and loudness A0 to search for prey. They can robotically adjust the frequency of their
released pulses and regulate the rate of pulse emission r ∈ [0; 1], with reference to the propinquity of
the goal.
c. Loudness will vary from a large (positive) A0 to a minimum constant value Amin.
Pteropus algorithm
Initialize the population
Pulse frequency defined in the range of Gi ∈ [Qmin, Gmax]
ri ,Ai are defined
While (t <Tmaximum)
By adjustment of frequency new solutions are generated
Obtained Solution & velocity are updated
If (random (0; 1) > ri )
Form the solution best one is selected
Around the best solution – a local solution will be engendered
End if
In arbitrary mode new solutions are generated
If (random (0; 1) < Ai and f (yi) < f(y))
New solutions are formed
ri and Ai values are increased
End if
Current best is found by ranking the Pteropus in order
End while
Output the optimized results
Virtual Pteropus are moved to form new solutions by the following,](https://image.slidesharecdn.com/03-20276-37709-1-ed-chaoticbased-editmukti-201021072836/85/Chaotic-based-Pteropus-algorithm-for-solving-optimal-reactive-power-problem-2-320.jpg)
![Int J Adv Appl Sci ISSN: 2252-8814
Chaotic based Pteropus algorithm for solving optimal reactive power problem (Lenin Kanagasabai)
267
Gi
(t)
= Gmin + (Gmax − Gmin) ∪ (0,1), (11)
li
(t+1)
= li
t
+ (yi
t
− best)Gi
(t)
, (12)
yi
(t+1)
= yi
(t)
+ li
(t)
(13)
Existing finest solution has been modified by the following,
y(t)
= best + ϵAi
(t)
(2U(0,1) − 1), (14)
When ri increases, Ai will decrease; when a Pteropus finds a prey & it mathematically written
as follows,
Ai
(t+1)
= αAi
(t)
, ri
(t)
= ri
(0)
[1 − exp(−γϵ)], (15)
To improve the Pteropus algorithm chaotic disturbance [19-21] is introduced. Here, variance 𝜎2
demonstrates the converge degree of all particles.
𝜎2
= ∑ [(𝑓𝑖 − 𝑓𝑎𝑣𝑔) 𝑓⁄ ]
2𝑁
𝑖=1 (16)
𝑓 = 𝑚𝑎𝑥 {1, 𝑚𝑎𝑥{|𝑓𝑖 − 𝑓𝑎𝑣𝑔|}} (17)
𝑦𝑖𝑑(𝑡 + 1) = 𝜇𝑦𝑖𝑑(𝑡)(1 − 𝑦𝑖𝑑(𝑡)) (18)
In order to augment the population diversity and prevent early convergence, adaptively chaotic
disturbance 𝑃𝑐 is added at the time of stagnation. Thus, 𝑃𝑐 𝑖𝑠 𝑚𝑜𝑑𝑖𝑓𝑖𝑒𝑑 𝑎𝑠 𝑃𝑐
′
.
𝐸𝑐𝑑
′
(𝑡 + 1) = 𝑝𝑐𝑑(𝑡) + 𝑍𝑖𝑑(2 𝑦𝑖𝑑(𝑡) − 1) (19)
𝑍𝑖𝑑 = 𝛽|𝑝𝑐𝑑(𝑡) − 𝐸𝑖𝑑(𝑡)| (20)
Chaotic based Pteropus Algorithm
Initialize the population
Pulse frequency defined in the range of Gi ∈ [Gmin, Gmax]
ri ,Ai are defined
While (t <Tmaximum)
By adjustment of frequency new solutions are generated
Obtained Solution & velocity are updated
Using the equations update the velocities and locations
Gi
(t)
= Gmin + (Gmax − Gmin) ∪ (0,1),
li
(t+1)
= li
t
+ (yi
t
− best)Gi
(t)
,
yi
(t+1)
= yi
(t)
+ li
(t)
If (random (0; 1) > ri )
Form the solution best one is selected
Around the best solution – a local solution will be engendered
End if
In arbitrary mode new solutions are generated
If (random (0; 1) < Ai and f (yi) < f(y))
New solutions are formed
ri and Ai values are increased
End if
Current best is found by ranking the Pteropus in order
End while
Output the optimized results](https://image.slidesharecdn.com/03-20276-37709-1-ed-chaoticbased-editmukti-201021072836/85/Chaotic-based-Pteropus-algorithm-for-solving-optimal-reactive-power-problem-3-320.jpg)
![ ISSN: 2252-8814
Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 265 – 269
268
4. SIMULATION RESULTS
Proposed Chaotic based Pteropus algorithm (CPA) has been tested in standard IEEE 14,300 bus
systems and comparison has been done with standard algorithms. Simulation output clearly indicates about
the efficiency of the proposed algorithm in reducing the real power loss.
At first in standard IEEE 14 bus system the validity of the proposed CPA algorithm has been tested
& comparison results are presented in Table 1.
Table 1. Comparison results
Control variables ABCO [22] IABCO [22] Projected CPA
V1 1.06 1.05 1.03
V2 1.03 1.05 1.00
V3 0.98 1.03 1.01
V6 1.05 1.05 1.00
V8 1.00 1.04 0.99
Q9 0.139 0.132 0.129
T56 0.979 0.960 0.969
T47 0.950 0.950 0.948
T49 1.014 1.007 1.002
Ploss (MW) 5.92892 5.50031 5.49842
Then IEEE 300 bus system [23] is used as test system to validate the performance of the proposed
CPA algorithm. Table 2 shows the comparison of real power loss obtained after optimization. Real power
loss has been considerably reduced when compared to the other standard reported algorithms.
Table 2 comparison of real power loss
Parameter Method EGA [24] Method EEA [24] Method CSA [25] Projected CPA
PLOSS (MW) 646.2998 650.6027 635.8942 627.1564
5. CONCLUSION
In this paper, chaotic based Pteropus algorithm (CPA) has been successfully solved the optimal
reactive power problem. Natural actions of Pteropus has been effectively imitated and modelled to solve
the problem. An adaptive chaotic disturbance 𝑃𝑐 is added at the time of stagnation Performance of
the Pteropus algorithm has been improved and better-quality solutions have been obtained. In addition,
exploration and exploitation capability of the proposed algorithm has been enhanced. Proposed CPA
technique has been tested in standard IEEE 14,300 bus systems & real power loss has been
considerably reduced.
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269
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Chaotic based Pteropus algorithm for solving optimal reactive power problem
- 1. International Journal of Advances in Applied Sciences (IJAAS) Vol. 9, No. 4, December 2020, pp. 265~269 ISSN: 2252-8814, DOI: 10.11591/ijaas.v9.i4.pp265-269 265 Journal homepage: http://ijaas.iaescore.com Chaotic based Pteropus algorithm for solving optimal reactive power problem Lenin Kanagasabai Department of EEE, Prasad V. Potluri Siddhartha Institute of Technology, India Article Info ABSTRACT Article history: Received Jan 8, 2020 Revised May 31, 2020 Accepted Jun 9, 2020 In this work, a Chaotic based Pteropus algorithm (CPA) has been proposed for solving optimal reactive power problem. Pteropus algorithm imitates deeds of the Pteropus. Normally Pteropus while flying it avoid obstacles by using sonar echoes, particularly utilize time delay. To the original Pteropus algorithm chaotic disturbance has been applied and the optimal capability of the algorithm has been improved in search of global solution. In order to augment the population diversity and prevent early convergence, adaptively chaotic disturbance is added at the time of stagnation. Furthermore, exploration and exploitation capability of the proposed algorithm has been improved. Proposed CPA technique has been tested in standard IEEE 14,300 bus systems & real power loss has been considerably reduced. Keywords: Chaotic Pteropus behaviour Optimal reactive power Transmission loss This is an open access article under the CC BY-SA license. Corresponding Author: Lenin Kanagasabai, Department of EEE, Prasad V. Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada, Andhra Pradesh, 520007, India. Email: [email protected] 1. INTRODUCTION To have secure & economic, operations of the power system optimal reactive power problem plays a prime role. Numerous conventional methods [1-6] have been successfully solved the problem. But difficulty found in handling the inequality constraints. Various types of evolutionary algorithms [7-18] applied to solve the problem. This paper projects chaotic Pteropus algorithm (CPA) for solving reactive power problem. Pteropus algorithm is designed based on the actions of Pteropus. while flying it avoid obstacles by using sonar echoes, particularly utilize time delay; happened while release and reflection of echo which has been utilized during the period of for course-plotting. In Projected algorithm echolocation feature is utilized in the algorithm and chaos theory intermingled in the flowing process. In order to augment the population diversity and prevent early convergence, adaptively chaotic disturbance 𝑃𝑐 is added at the time of stagnation. Projected CPA algorithm has been tested in standard IEEE 14,300 bus systems & simulation study show the best performance of the projected algorithm in reducing the real power loss. 2. PROBLEM FORMULATION The key objective of the reactive power problem is to minimize the system real power loss & given as, Ploss= ∑ gk(Vi 2 +Vj 2 −2Vi Vj cos θij ) n k=1 k=(i,j) (1)
- 2. ISSN: 2252-8814 Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 265 – 269 266 Voltage deviation magnitudes (VD) is stated as follows, Minimize VD = ∑ |Vk − 1.0|nl k=1 (2) Load flow equality constraints: PGi – PDi − Vi ∑ Vj nb j=1 [ Gij cos θij +Bij sin θij ] = 0, i = 1,2 … . , nb (3) QGi − QDi − Vi ∑ Vj nb j=1 [ Gij sin θij +Bij cos θij ] = 0, i = 1,2 … . , nb (4) Inequality constraints are: VGi min ≤ VGi ≤ VGi max , i ∈ ng (5) VLi min ≤ VLi ≤ VLi max , i ∈ nl (6) QCi min ≤ QCi ≤ QCi max , i ∈ nc (7) QGi min ≤ QGi ≤ QGi max , i ∈ ng (8) Ti min ≤ Ti ≤ Ti max , i ∈ nt (9) SLi min ≤ SLi max , i ∈ nl (10) 3. PTEROPUS ALGORITHM Pteropus algorithm imitates deeds of the Pteropus. Normally Pteropus while flying it avoid obstacles by using sonar echoes, particularly utilize time delay; happened while release and reflection of echo which has been utilized during the period of for course-plotting. Generalized rules for Pteropus algorithm are: a. To sense the distance- all Pteropus use echolocation b. In arbitrarily mode Pteropus fly with velocity ϑi at position yi with a fixed frequencyfmin, varying wavelength λ and loudness A0 to search for prey. They can robotically adjust the frequency of their released pulses and regulate the rate of pulse emission r ∈ [0; 1], with reference to the propinquity of the goal. c. Loudness will vary from a large (positive) A0 to a minimum constant value Amin. Pteropus algorithm Initialize the population Pulse frequency defined in the range of Gi ∈ [Qmin, Gmax] ri ,Ai are defined While (t <Tmaximum) By adjustment of frequency new solutions are generated Obtained Solution & velocity are updated If (random (0; 1) > ri ) Form the solution best one is selected Around the best solution – a local solution will be engendered End if In arbitrary mode new solutions are generated If (random (0; 1) < Ai and f (yi) < f(y)) New solutions are formed ri and Ai values are increased End if Current best is found by ranking the Pteropus in order End while Output the optimized results Virtual Pteropus are moved to form new solutions by the following,
- 3. Int J Adv Appl Sci ISSN: 2252-8814 Chaotic based Pteropus algorithm for solving optimal reactive power problem (Lenin Kanagasabai) 267 Gi (t) = Gmin + (Gmax − Gmin) ∪ (0,1), (11) li (t+1) = li t + (yi t − best)Gi (t) , (12) yi (t+1) = yi (t) + li (t) (13) Existing finest solution has been modified by the following, y(t) = best + ϵAi (t) (2U(0,1) − 1), (14) When ri increases, Ai will decrease; when a Pteropus finds a prey & it mathematically written as follows, Ai (t+1) = αAi (t) , ri (t) = ri (0) [1 − exp(−γϵ)], (15) To improve the Pteropus algorithm chaotic disturbance [19-21] is introduced. Here, variance 𝜎2 demonstrates the converge degree of all particles. 𝜎2 = ∑ [(𝑓𝑖 − 𝑓𝑎𝑣𝑔) 𝑓⁄ ] 2𝑁 𝑖=1 (16) 𝑓 = 𝑚𝑎𝑥 {1, 𝑚𝑎𝑥{|𝑓𝑖 − 𝑓𝑎𝑣𝑔|}} (17) 𝑦𝑖𝑑(𝑡 + 1) = 𝜇𝑦𝑖𝑑(𝑡)(1 − 𝑦𝑖𝑑(𝑡)) (18) In order to augment the population diversity and prevent early convergence, adaptively chaotic disturbance 𝑃𝑐 is added at the time of stagnation. Thus, 𝑃𝑐 𝑖𝑠 𝑚𝑜𝑑𝑖𝑓𝑖𝑒𝑑 𝑎𝑠 𝑃𝑐 ′ . 𝐸𝑐𝑑 ′ (𝑡 + 1) = 𝑝𝑐𝑑(𝑡) + 𝑍𝑖𝑑(2 𝑦𝑖𝑑(𝑡) − 1) (19) 𝑍𝑖𝑑 = 𝛽|𝑝𝑐𝑑(𝑡) − 𝐸𝑖𝑑(𝑡)| (20) Chaotic based Pteropus Algorithm Initialize the population Pulse frequency defined in the range of Gi ∈ [Gmin, Gmax] ri ,Ai are defined While (t <Tmaximum) By adjustment of frequency new solutions are generated Obtained Solution & velocity are updated Using the equations update the velocities and locations Gi (t) = Gmin + (Gmax − Gmin) ∪ (0,1), li (t+1) = li t + (yi t − best)Gi (t) , yi (t+1) = yi (t) + li (t) If (random (0; 1) > ri ) Form the solution best one is selected Around the best solution – a local solution will be engendered End if In arbitrary mode new solutions are generated If (random (0; 1) < Ai and f (yi) < f(y)) New solutions are formed ri and Ai values are increased End if Current best is found by ranking the Pteropus in order End while Output the optimized results
- 4. ISSN: 2252-8814 Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 265 – 269 268 4. SIMULATION RESULTS Proposed Chaotic based Pteropus algorithm (CPA) has been tested in standard IEEE 14,300 bus systems and comparison has been done with standard algorithms. Simulation output clearly indicates about the efficiency of the proposed algorithm in reducing the real power loss. At first in standard IEEE 14 bus system the validity of the proposed CPA algorithm has been tested & comparison results are presented in Table 1. Table 1. Comparison results Control variables ABCO [22] IABCO [22] Projected CPA V1 1.06 1.05 1.03 V2 1.03 1.05 1.00 V3 0.98 1.03 1.01 V6 1.05 1.05 1.00 V8 1.00 1.04 0.99 Q9 0.139 0.132 0.129 T56 0.979 0.960 0.969 T47 0.950 0.950 0.948 T49 1.014 1.007 1.002 Ploss (MW) 5.92892 5.50031 5.49842 Then IEEE 300 bus system [23] is used as test system to validate the performance of the proposed CPA algorithm. Table 2 shows the comparison of real power loss obtained after optimization. Real power loss has been considerably reduced when compared to the other standard reported algorithms. Table 2 comparison of real power loss Parameter Method EGA [24] Method EEA [24] Method CSA [25] Projected CPA PLOSS (MW) 646.2998 650.6027 635.8942 627.1564 5. CONCLUSION In this paper, chaotic based Pteropus algorithm (CPA) has been successfully solved the optimal reactive power problem. Natural actions of Pteropus has been effectively imitated and modelled to solve the problem. An adaptive chaotic disturbance 𝑃𝑐 is added at the time of stagnation Performance of the Pteropus algorithm has been improved and better-quality solutions have been obtained. In addition, exploration and exploitation capability of the proposed algorithm has been enhanced. Proposed CPA technique has been tested in standard IEEE 14,300 bus systems & real power loss has been considerably reduced. REFERENCES [1] K. Y. Lee, et al, “Fuel-cost minimisation for both real and reactive-power dispatches,” Proceedings Generation, Transmission and Distribution Conference, vol. 131, no. 3, pp. 85-93, 1984. [2] N. I. Deeb, et al., “An efficient technique for reactive power dispatch using a revised linear programming approach,” Electric Power System Research, vol. 15, no. 2, pp. 121-134, 1988. [3] M. R. Bjelogrlic, M. S. Calovic, B. S. Babic, et. al., “Application of Newton’s optimal power flow in voltage/reactive power control,” IEEE Trans Power System, vol. 5, no. 4, pp. 1447-1454, 1990. [4] S. Granville, “Optimal reactive dispatch through interior point methods,” IEEE Transactions on Power System, vol/issue: 9(1), pp. 136–146, 1994. [5] N. Grudinin, “Reactive power optimization using successive quadratic programming method,” IEEE Transactions on Power System, vol. 13, no. 4, pp. 1219-1225, 1998. [6] Wei Yan, J. Yu, D. C. Yu and K. Bhattarai, “A new optimal reactive power flow model in rectangular form and its solution by predictor corrector primal dual interior point method,” IEEE Trans. Pwr. Syst.,vol. 21,no. 1, pp. 61-67, 2006. [7] Aparajita Mukherjee, Vivekananda Mukherjee, “Solution of optimal reactive power dispatch by chaotic krill herd algorithm,” IET Gener. Transm. Distrib, vol. 9, no. 15, pp. 2351-2362, 2015. [8] Hu, Z., Wang, X. & Taylor, G. “Stochastic optimal reactive power dispatch: Formulation and solution method,” Electr. Power Energy Syst., vol. 32, no. 6, pp. 615-621, 2010. [9] Mahaletchumi A/P Morgan , Nor Rul Hasma Abdullah, Mohd Herwan Sulaiman, Mahfuzah Mustafa and Rosdiyana Samad, “Computational intelligence technique for static VAR compensator (SVC) installation
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